Sctp-based transmission of data-partitioned H.264 video
Bibliographic record
Abstract
H.264 is the most recent standard for video compression, achieving not only the highest compression efficiency but also providing network friendliness and error resiliency. Data partitioning is one of the very important error resilience features of H.264. With data partitioning, each video slice is encoded into three different units of data with different importance. The encoded partitions containing the most important information should be protected against transmission error to ensure good picture quality. By virtue of multistreaming and partial reliability property of Stream Control Transmission Protocol (SCTP), we can set different priority or reliability levels for different data partitions. In this article, we investigate the impact of the loss of different partitions on picture quality. We present a comparative study of the possible solutions for transmission of H.264 video using SCTP, considering both partitioned and non-partitioned H.264 video. We demonstrate how reliability features of SCTP can be efficiently mapped to the error resiliency features of H.264 video.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".